Finding hidden growth opportunities in your product | Albert Cheng (Duolingo, Grammarly, Chess.com)

The Nature of Growth

Albert Cheng: Growth as the job is to connect users to the value of your product. Growth sometimes gets this reputation that it’s just pure metrics hacking.

Biggest Monetization Breakthrough

Lenny Rachitsky: You’ve worked at three of the most successful consumer subscription products in the world. What do you think is the biggest missing piece that people don’t get about building a successful consumer subscription product?

Albert Cheng: User retention is gold for consumer subscription companies. If you don’t retain your users, then a lot of the onus is on getting them to pay on day one.

Counterintuitive Team Building Insights

Lenny Rachitsky: Noam Levinsky, he said that I need to ask you about the biggest monetization win that you found at Grammarly.

Albert Cheng: The lived product experience for most of the free users was that Grammarly was just a product to fix your spelling and grammar because those were the free suggestions. What if we actually sampled a number of different paid suggestions and interspersed them to free users across their writing? All of a sudden, people were seeing Grammarly as a much more powerful tool than they were before.

About the Guest

Lenny Rachitsky: What’s the most counterintuitive lesson you’ve learned about building teams?

Albert Cheng: I saw some of the highest performers just being people that had very high agency, had that clock speed, had that energy, but they didn’t necessarily need to have deep experience on that matter. Sometimes experience could be a crutch, especially in this world where the grounds are shifting so fast with AI. A lot of your learned habits actually need to be intentionally discarded.

Conversation Framework and Upbringing

Lenny Rachitsky: Today my guest is Albert Cheng. Albert is known as one of the top consumer growth minds in the world. He led growth and monetization at three of the most successful and beloved consumer products in the world, Duolingo, Grammarly, and now Chess.com. Earlier in his career at YouTube, he worked on streaming and gaming features used by over 20 million people.

His unique approach to growth blends marketing, data, strategy, and product management, and in our conversation, we cover a lot of ground, including his explore and exploit framework to find growth opportunities. His biggest and most interesting growth wins at Duolingo, Grammarly and Chess.com, how he uses AI to accelerate his growth work, what he’s come to realize about the power of brand and community in your growth work, his top experimentation, best practices, why his goal at every company is to run 1,000 experiments a year and so much more.

A huge thank you to Erik Allebest, Noam Levinsky, and Jorge Mazal for suggesting topics for this conversation. If you enjoy this podcast, don’t forget to subscribe and follow it in your favorite podcasting app or YouTube, it helps tremendously. Also, if you become an annual subscriber of my newsletter, you get 15 incredible products for free for an entire year, including Lovable, Replit, Bolt, n8n, Linear, Superhuman, Descript, Wispr Flow, Gamma, Perplexity, Warp, Granola, Magic Patterns, Raycast, ChatPRD and Mobbin.

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Albert Cheng: Thanks for having me, Lenny. Excited to be here.

Parallels Between Piano and Growth

Lenny Rachitsky: I’m even more excited to have you here. So as I do for every podcast conversation, I reached out to a bunch of people that you’ve worked with that know you well to find out what to ask you about and what topics to spend time on. Jorge Mazal, who is famous in my world for writing, what was for the longest time, the most popular newsletter post on my newsletter, it’s actually people have usurped it now, but it was stuck there for a long time. So here’s what he wrote. “It is a mystery to me how Albert is able to do what he does. I am actually eager to listen to this episode and learn from him.”

The Explore and Exploit Framework

Albert Cheng: That is super nice. Thank you, Jorge. I’ve learned so much from him. I’m the type of weird person that likes to wake up before their kids and pull up a bunch of browser tabs and look at experiments. So it was perfect that Jorge brought me into the growth world at Duolingo, learned a ton of best practices, and he’s just a great guy. Thanks, Jorge.

When to Explore or Exploit

Lenny Rachitsky: We’re already getting into these tactics. I love it. Let me just give a little framing on what I want to do with this conversation. What I want to try to do is to help people learn tools and mental models for finding growth opportunities for their own products and essentially learn the growth mentality that you bring into the companies and products that you work on.

What I want to start with is to give us a little insight into how you became what you became. There’s an interesting pattern I found across a bunch of recent guests, which is many people were very good at piano when they were younger and were very serious piano players. For example, Head of ChatGPT, Nick Turley was almost going to become professional jazz pianist. You were very serious as a piano player earlier in your career. How did you go from pianist to one of the top growth minds in the world briefly?

Albert Cheng: Well, that’s very flattering, but I appreciate it. Yeah, I grew up playing a lot of piano. My parents were immigrants from Taiwan and I was the oldest kid that they had, and so I definitely felt that strong encouragement, if you will, to learn a bunch of things, take them seriously, study hard, and so I did. And my parents, even though they weren’t musically proficient, they had a deep love for classical music.

So I was the stereotypical baby that would listen to Mozart, I guess when I was sleeping type of thing. And I still vividly remember we had this upright Yamaha piano, and at the very top of the piano we had this countdown clock from 90 minutes. Literally every single day of my childhood, just practice really, really consistently.

At first, I really was irritated by that thing, but as I grew older, I started to appreciate music quite a bit more. But anyway, I think what really accelerated my interest and abilities in piano was I feel like I hit the lottery. I had perfect pitch, and so I was able to quickly understand whether I was playing the right stuff or the wrong stuff and just pick up music pretty rapidly.

Applying AI in Experiments

Lenny Rachitsky: What does perfect pitch even mean? Does that mean which note is playing?

AI Prototyping and Toolchains

Albert Cheng: Exactly.

Finding Growth and Monetization Wins

Lenny Rachitsky: Okay.

Why Choose the Freemium Model

Albert Cheng: Exactly.

Lenny Rachitsky: Wow.

Reverse Trials vs. Free Trials

Albert Cheng: So I could listen to a song and then just a very, very clear understanding of which note I’m supposed to start with and if I’m playing something wrong. So it’s very helpful.

Success Factors for Consumer Subscriptions

Lenny Rachitsky: Unfair.

Retention Metrics Rule of Thumb

Albert Cheng: It’s unfair. Definitely. So anyway, yeah, I got quite good as a teenager in high school and even considered studying at a music conservatory. My intrinsic motivation for music wasn’t necessarily as strong at that point, and so I decided to go to engineering school instead, but that would’ve been an incredibly different career. And to your original point around the relationship between music and growth, I didn’t really reflect on this until recently.

I have a four-year-old and I’m starting to teach him how to bang on the keys a little bit, but a couple things stand out. One is that I think music and growth, they both rely on this just consistent repetition. You’re constantly making mistakes. You have this super tight feedback loop. You have to get really resilient to just making mistakes all the time. And you know that the way of learning is through those mistakes. So that’s a thing that I learned very early, and the second thing that occurred to me is that they both have this structural underpinning to them.

With growth, you have a growth model, you have metrics, you have experiments, you have channels, things like that. But you also need on a day-to-day basis to have creativity, you got to come up with interesting solutions or hypotheses to test. And the same is true on the music side. You have music theory of scales and stuff, but to create beautiful music, you need that passion, that emotion, that flow. So I think that’s the beautiful combination between the two.

Lenny Rachitsky: Fun fact, my wife bought me piano/singing lessons for Father’s Day recently, and I’ve gotten really into this stuff. So I’m learning how to play very basic piano now and learning to identify notes and hit notes with my voice.

Retaining Existing Users: Greater Leverage

Albert Cheng: Nice.

Lenny Rachitsky: What a weird new thing.

Re-engaging Dormant Users

Albert Cheng: Could be your next act.

Lenny Rachitsky: It could be. I could go the reverse, I could become a professional piano player. Oh man. No, it’s so fun, so hard though. I’m just like, my fingers are like, how do you do four freaking keys at once? I’m just like “What is going on here?” Okay, so let’s get into the meat of it. I want to talk about growth.

There’s a very specific framework that as we were chatting that I think would be really helpful for people to hear and learn from you. You call it explore and exploit. I think there’s a bunch of different ways to think about this. Talk about this framework and how that informs the way you think about growth.

Operational Differences Across Three Companies

Albert Cheng: Yeah, I initially came up or heard with, heard about explore and exploit through my engineering partner at Grammarly, Nermal, and I think he actually had taken some reforged classes. So maybe the original inventor of it might be Brian Balfour, who I know has been on your pod. But anyway, it’s a great concept.

The gist of it is that when you’re in exploratory mode, think of it as finding the right mountain to climb. And then when you’re in exploitation mode, it’s like focusing your resources on climbing that mountain effectively. And certain companies, I think the warning is to basically spend too much of your time on one end of the spectrum. If you do too much exploration, you can have your team feel a little bit too scattershot, just trying a hundred different random ideas.

What’s the through line? What’s the strategy? How do you pattern match successes across them? And if you do too much in exploitation, which is often the MO of growth teams, it can lead to this saturation and stagnation where you’re just locally maximizing a thing. And even though this principle of explore and exploit, it’s typically thought of as a macro thing. I like to work with my teams more on the insight level. So I’ll give you a concrete example.

So I work at chess.com and one of our priorities is to encourage chess players to improve, to learn and improve. So one of the PMs that we have, Dylan, he works on all the learning features. The most used learning feature in our product is called game review. So you play a game of chess, after the game is over, we have this virtual coach that teaches you about your worst moves, best moves, et cetera. And his job is to improve user engagement and retention.

And so he’s in this exploratory phase trying to figure out how do I drive more of that type of activity? And what he observes is that 80% of people that review their games actually do so after a win. And that’s really counterintuitive to when we initially built the feature. We thought that people would want to use it after losses or to see their mistakes such they could work on their mistakes. That turned out not to be the truth when it came to the human psychology and the actual data of it. And so we made some changes in the product experience.

When you lose a game now as opposed to surfacing your blunders and your horrible stuff that you did, we flip it on its head and so we show you your brilliant moves, your best moves, and we have coach say something encouraging, “Losing, just part of learning, keep it up.” That type of thing. That change alone was pretty dramatic for us.

It grew game reviews by 25%, subscriptions by 20%, user retention by a lot as well. So that was fantastic, but the point is that it doesn’t just stop there. You have to take that insight, share it broadly across the company. Now, adjacent product managers like the PM working on puzzles can now think about, “Okay, how do I audit these cold patterns in my product and think about making them more positive?”

I can change the success rating, I could tweak some copy, change the color of some buttons, and so you now can take this experiment win and expand it out 10X across your organization and that’s the kind of exploitation phase of it. So when done right, you can oscillate between the two until you saturate out of exploitation mode and then you encourage the teams to brainstorm and get more creative again.

Chess.com: One Hundred Percent Chess

Lenny Rachitsky: Amazing. Okay, so there’s a lot here to follow up on. One is the core piece of advice when you find something that works really well, find ways to build on that learning. One is here’s an insight, it can apply to other parts of the product. “Hey teams, here’s something we learned unexpected. Maybe this can help you. Also, just keep find more, run more experiments in the same zone.” I imagine is a part of that.

Brand and Growth Synergies

Albert Cheng: Yeah, exactly right. In my experience, the typical win rate, and I hate to use that term for experiments, is often something like 30 to 50%. Usually you’re trying a bunch of things, a lot of hypotheses turn out not to be true, consumer products are very unpredictable like that, but when you do find a thing that breaks through the noise, and it could actually be a hugely losing experiment too, those are also super valuable.

Surfacing those across the company, the original PM running that experiment doesn’t necessarily need to be the person that figures out what you should do for all the other parts of your product experience, but the onus is on them to clearly articulate what their hypothesis is, what they found such that then as a growth leader, I can encourage people to swarm around that and try a bunch of different ideas such that the success rate is up and the impact is up. So it’s just oscillating back and forth between the two. That is the magic bullet.

AI Impact on Product and Growth

Lenny Rachitsky: I think another takeaway here/something that I think about when I hear what you’re saying is there’s often a lot more wins in an area than people expect that you can continue to find wins and growth in something for a long time.

The Relationship Between AI and Chess

Albert Cheng: Exactly right. Yes. At the end of the day, users, I think within a company sometimes you can have this siloed approach where you break apart the product experience in 50 different ways and distribute them across different teams, and you assume that users interact with each of the different features with a different mentality, but oftentimes that’s actually not necessarily the case. And so sometimes, you can surface an insight that’s more human psychology based that can resonate across the entire product experience. And so I think when you can find that, you can double down.

How AI Changes Growth Team Work

Lenny Rachitsky: People hearing this might feel like, “Okay, yes, find big wins and then find more.” Is there something you find that helps you figure out when to explore versus when to exploit when you’ve exploited too far? Just like any heuristics or I don’t know, ways of helping people guide them along this process of exploring and exploiting?

Albert Cheng: One thing that I try to focus on at a company of our scale of a chess.com, right? We’re running roughly 250 experiments a year. So we’re not the highest in the industry, but we run a decent volume. And so when that happens, I invest in these experiment explorer tools and we can talk about AI as well as another way to uncover and pick out these nuggets of wisdom, but basically, these explorer tools can allow me to look across the spectrum of experiments that are going on.

Try to figure out if there are patterns between the hypotheses and the learnings that are happening. And if I’m starting to see more and more experiments that are not statistically significant, that may be a signal to me to say, “Okay, we might’ve tried to exploit a little bit too far. There might not be as much juice to squeeze. Hey guys, let’s get back to the table and brainstorm and be a little bit more divergent with our thinking.”

Defining the Essence of Growth

Lenny Rachitsky: Well, let me follow this thread on AI and how you’re using AI to help you figure this out. That is very cool. Talk about that.

Albert Cheng: I think one of the latest things that we’ve been tinkering around with is this text to SQL capability. It’s actually pretty powerful. We have this data request Slack channel where for the longest time, and this is still true today, people will toss in all sorts of just one-off questions. How many subscribers do we have in South Africa? Or how long did somebody play puzzles last month or something?

And these ad hoc questions, they often take a lot of human time to just go in and a data analyst needs to prioritize it and find time to go run the query. And yes, you can invest in self-serve tooling to improve at this, but also I found that AI is quite good at doing that first pass answer as well. And so we’re working on training some of these Slack bots to essentially be the first party provider of a lot of these answers, which makes the company as a whole lot more data informed, I guess.

And I think what’s also kind of interesting is that just human nature is that if you have a question that you feel like you might be a bit embarrassed to ask or you don’t want to bother someone, you just don’t ask the question. And so by the nature of having these tools, you get actually a pretty large explosion of questions being asked. And I think you see this in ChatGPT too, right? It’s like just having a thing that you can converse with that you feel comfortable in makes a huge difference.

Leveling Up Experimentation Capabilities

Lenny Rachitsky: Okay, this is extremely cool. So is this something you build basically it’s a Slack bot that gives you the SQL query or does it actually do the analysis for you?

Albert Cheng: No, it does the analysis. Yeah.

North Star for a Thousand Experiments

Lenny Rachitsky: Whoa, so cool. Okay. Is this something you guys are going to release or is this just like somebody, you guys should just build this at every company?

Experiment Systems Over Single Experiments

Albert Cheng: We should. It’s a good idea.

A Proud Experiment Success Story

Lenny Rachitsky: Okay. Okay. Well, there’s an episode where everyone in the comments is like, “Open source this.” So we’ll see if that happens again. That is very cool. Are there other examples of that kind of stuff that you’ve done or seen?

Albert Cheng: An adjacent example is a lot of the product managers, we’re tinkering around with all sorts of different prototyping tools right now. It’s just like go from an idea to a representative solution. Today, there’s a lot of humans involved in taking an idea, writing up a spec, doing a review, doing design, et cetera. I’m sure you’ve interviewed plenty of people that have talked about this specific problem.

And so for us, we’ve invested a bit in at least carving out the main screens of our product experience, things like our onboarding flow, our home screen, our chessboard as an example, and building essentially AI prototypes of those using tools like a V0 or a Lovable. And when you have those foundational pieces, you can then share them with the rest of the company and they can use that as a starting point and then they can try to put their ideas on top of that and then they become a lot more discussable and hopefully testable relatively soon.

Sparking Motivation and Building Habits

Lenny Rachitsky: What’s in your AI stack along those lines?

The Most Counterintuitive Team Lesson

Albert Cheng: The PMs are mostly using V0. The designers love Figmas, they’re using Figma Make. The engineers are using a combination of tools right now. So Cursor, Cloud Code, GitHub, Copilot. Marketing teams use all sorts of tools for translation, subtitles, content adaptations, et cetera. Customers support uses Intercom then. So there’s quite a lot of tools that are used across the company.

I would say though that something that is annoying to me is that we haven’t yet figured out the bridging from the tinkering to the workflow quite as seamlessly as I would like. And so each sub-function, even though the common I guess wisdom now is that AI is going to strip away these functional titles. It is true that based on your experience, you may gravitate to using a type of tool more. And if that tool isn’t as interoperable with some of the other tools that you need to pass down the chain to actually ship it into production, at least at our scale.

I think for smaller startups, sure, PMs should just go ship it, but for us, we are still doing some handoffs between functions. I expect that to change over time and we are investing in some of design system components and MCPs and stuff to make it a little bit easier. But yeah, it’s an investment and it takes time to smooth things out.

Company Size and Personal Fit

Lenny Rachitsky: I want to come back to this topic of how things have changed and how you work as a product person, as a growth person across the companies you’ve been at. But first of all, I want to talk about another example of finding growth wins and monetization wins. Noam Levinsky, who is Chief Product Officer at Grammarly, you worked with him for a while while you were at Grammarly. He said that I need to ask you about the biggest monetization win that you found at Grammarly and how you discovered the opportunity.

Albert Cheng: I had the pleasure of working with Noam and his product team at Grammarly. Some context first for those that don’t use Grammarly. So Grammarly is an AI-powered writing assistant. And so typically, people will use it as a Chrome extension or a downloadable desktop client. And basically what it does is it overlays your writing with a bunch of different-

Corners of Failure

Lenny Rachitsky: I use it. I’m a big fan. I use it-

Albert Cheng: Correction, so you’re a big fan.

Lightning Q&A Round

Lenny Rachitsky: … And it saves my life.

Albert Cheng: Fantastic. Glad to hear that. Grammarly is a freemium business model, which means that over 90% of our users are on the free service and the rest of it pay for subscriptions essentially, right? And so one of the teams, they work on subscriber conversion, PM there is Kayla, that team is great and their job is to figure out the free to paid subscription path.

And so one of the realizations, one, is that we weren’t actually tracking the events that well for the types of essentially suggestions that people were getting and how often were users seeing paywalls and stuff like that. That’s kind of step number one. We have to put that instrumentation in. Step number two is that, “Hey, we noticed, actually first let me explain some of the logic.”

So as a free user, you basically get these underlines across your writing and if you accept all of them, then you see the paywall and that encourages you to subscribe for more nuanced features. As a free user, the main things you get are spelling, grammar, they’re basically correctness things. And as a paid user you get that, how do you improve your tone to be more empathetic? How do you improve your writing to be more clear?

How can you rewrite entire sentences, that type of thing. And so the observed behavior from all that tracking and data was that actually a very small percentage of our free users was deciding to accept all of their suggestions. They were more picking and choosing as they go, and I wonder if your experience is kind of similar too.

Lenny Rachitsky: Definitely, yeah. I’m always like, “Wait, stop rewriting everything.” Just like this part is wrong. I will fix it. Yeah, I’m very much a pick and choose.

Albert Cheng: That’s right.

Lenny Rachitsky: Correction person.

Albert Cheng: And then the second thing, which is I think equally if not more interesting is that I was at this company during this generative AI transformation, which is obviously still going on. And quite frankly, both the company brand as well as the lived product experience for most of the free users was that Grammarly was just a product to fix your spelling and grammar because those were the free suggestions we were showing people.

And so we decided to flip that on its head entirely and we said, “Okay, what if we actually sampled a number of different paid suggestions and interspersed them to free users across their writing?” Such that they were intermingled and we would provide a limited taste of what the paid offering had to provide. And on the surface, even though it’s rational, the concern is that if we give too much of this away, then will people want to subscribe?

And we found completely that was not the case all of a sudden, people were seeing Grammarly as a much more powerful tool than they were before and our upgrade rates nearly doubled just through this change. And so I think this is interesting, just modernization learning that especially if you work on a freemium product, try to have your free product be a reflection of everything that your product can offer you. Obviously to an extent there’s some costs involved with some of the paid features and things like that, but it generally will pay for itself if you’re able to put your best foot forward and go do that. So that really worked well for us there.

Lenny Rachitsky: I think this is what converted me to being a paid Grammarly subscriber. Wow, what a genius move. So essentially, it’s here’s a bunch of improvements, but you get three, I think max, and then it’s like, “Okay, now you get upgrade.”

Albert Cheng: It’s basically a reverse free trial but in real time while you’re writing as opposed to a time-based one. So we adopted some patterns that are in the industry, but molded it to Grammarly’s specific use case.

Lenny Rachitsky: Right. I was going to ask, so it’s not like a full trial, it’s like a capped trial where you get a certain number of things and then you run out and then they get refreshed. I think once a day or something like that is what I found.

Albert Cheng: Yeah, you got it.

Lenny Rachitsky: Yeah. Grammarly is the best/most devious at their upsells. I’m always just like, “God damn it, I’m so close to seeing an improvement, I just have to upgrade.” And it’s right there, it’s right there where my mouse is.

Albert Cheng: Yeah, well, I’m not proud of being devious, but.

Lenny Rachitsky: In really getting me to buy the thing. Good job. What was it? Kayla? Okay, nice job Kayla. It’s very effective. I love that. Okay, so in terms of the free trial, I don’t know, is there anything there of just, there’s always this question of freemium, give things away and then there’s pro account, there’s like trial versus time. Some features are limited. I don’t know, do you have for consumer subscription products like here’s the way to go?

Albert Cheng: Yeah, I think first of all, why do freemium subscription in the first place is a common question that I’ve joined all these companies that are freemium subscription. What do I like about it I guess? Well one, I think it ties really nicely to mission orientation of a lot of these companies. It’s often like you want to spread the product as wide as possible because that’s why the founders built the thing, right?

You’re trying to improve education with Duolingo or Grammarly or Chess.com, these are meant to be widespread products with a really wide value proposition that fits globally. And so obviously, the lowest friction to that is going to be a free product. So that alone is part of it. Another part of it is that a lot of these products primarily grow through word of mouth and especially if you can build network effects in the product, like Duolingo has a bunch of social features or with Grammarly, they have a bit of a B2C2B play as well.

So you see Grammarly being used by teams and by companies and whatnot, and even if users are on the free plan, they still provide quite a lot of value in making sure that Grammarly can be purchased by a coworker or by a team member or whatever. So I think these things are usually why I lean toward make sure that the core value proposition that you’re providing users is free and is permanently free and then you layer on a sampling or a taste of some of the premium features that are on top of it. That’s usually the sweet spot that I’ve seen.

As to the trials, reverse trials type of thing, I think it largely depends. I think if you have especially a B2B feature where you may have some lock-in, reverse trials can be super powerful. You just want to get people in there. You don’t need to ask for their credit card because they’re using your CRM or they’re investing quite a lot of time in building out material and content. And so by the time that window drops, you actually feel, “Oh man, I probably should keep this and start paying.” I think for a lot of consumer products it’s a little bit harder for that to work. And so I’ve typically seen more just normal free trials be the norm.

Lenny Rachitsky: Let me follow this thread of just consumer subscription products. I feel like this is the category that every indie developer dreams of building a product in because it’s easy to build. Cool, I’ll build an app, I add a paywall, and then they realize this is a lot harder than I thought. From a perspective of distribution and CAx and growth like that, is that the biggest missing piece that people don’t get about building a successful consumer subscription product?

Albert Cheng: Yeah, user retention is gold for consumer subscription companies. If you don’t retain your users, then a lot of the onus is on getting them to pay on day one, that’s super hard. Then you’re dealing with totally different business models where you’re paying for users, you’re trying to aggressively upsell them before they hit any habitual usage patterns with your product.

A lot of apps naturally do that because that’s how they break the mold and get their first users to do it, but I don’t know, I’ve been fortunate to join companies after that initial phase, but especially take Duolingo and Chess.com, these are organic word of mouth driven businesses and in both ways, they grew the market from a much smaller market and as opposed to it being a very competitive space where you’re competing and taking market share from others and bidding for higher terms and stuff like that. So I don’t know, there’s something to that.

Lenny Rachitsky: So what I’m hearing here is you need to find a way to grow through word of mouth for this to have any chance of success and also retention needs to be very high. Do you have a heuristic of what retention needs to be for you to have a chance building a successful consumer subscription business?

Albert Cheng: I think consumer companies tend to track essentially two main types of user retention. There’s more of the new user, one, D1, D7, et cetera. I think when you have your D one retention somewhere around the 30 or 40% mark, that’s quite solid I think for a consumer app. If it’s much lower than that, then sometimes I might question the intent of the user or the ability for that, you to I guess acquire just mathematically acquire enough users such that you can grow a big enough daily active user base.

Lenny Rachitsky: That’s surprisingly low.

Albert Cheng: Yeah.

Lenny Rachitsky: So it feels achievable in theory.

Albert Cheng: It’s achievable. It’s achievable in theory, but there are so many options out there in the market and people are feeling a lot of app and product bloat.

Lenny Rachitsky: And so just to be clear, you’re saying 20 to 30% of people come back the next day?

Albert Cheng: Yeah, 30 to 40.

Lenny Rachitsky: 30 to 40.

Albert Cheng: 40%. I think you’re an okay place. I think even more importantly, and you mentioned Jorge to kick this off, but he wrote that very, very popular article about the growth model and how current user retention rate was the biggest thing for them. And I think especially if you have a product that has daily frequency, that’s actually the retention that matters the most is that of your existing user base that has developed a habitual pattern, how sticky is your product? And it’s that retention rate that really compounds and builds that daily habit.

So over time, especially when companies mature a little bit, you actually focus most of your energy on the existing user retention mechanics. You find that that’s a much, much bigger lever. One exception is that Grammarly was a different type of product and that you install it and you don’t proactively open it every day. So that was interesting to me because I assumed that you should always just focus on existing user retention, but for a product like Grammarly, it’s actually the activation installation aha moment that’s really, really critical and will carry the user for a very, very long time.

Lenny Rachitsky: That makes sense. Yeah, the stats would show someone’s a daily active user because they’re typing things and that’s not an accurate step for Grammarly. The other interesting trend I’ve noticed across successful consumer subscription products is they always start very scrappy and very cost-efficient and spend efficient because I think it’s because it takes them a long time to find something that’s working and they’re surviving on that margin of retention to growth cost essentially.

Albert Cheng: Yeah, that’s right.

Lenny Rachitsky: Yeah, and the retention piece, that’s such a good point. My newsletter is very much along these lines. It’s just like how many people are joining every day, how many people are leaving? And it’s a difficult treadmill to be on because people, they want to save money, they want to spend on Netflix and things like that. So as amazing as you are, people are always going to leave. So the trick is how do you find more people coming than going?

Albert Cheng: Yeah, and I think just to take Chess.com example, I think probably 80% of our daily or weekly active users, I’ll check the numbers, but something like that would be a current user or an existing user and then a new and a reactivated or resurrected user. Those are actually about similar size for a company of our sale. So even though there’s a lot of attention on that new user experience, it’s actually pretty interesting that the components of your active user base are actually not heavily weighed in the new user set after you mature to a certain degree.

Lenny Rachitsky: Can you explain that a little bit more?

Albert Cheng: Yes. So after some period of time, you stack up a lot of inactive users in your product and you also stack up sporadic users, people that may not have a daily habit, but they will use it once or twice a week or once or twice a month type of thing. And so eventually that math adds up where you have, let’s say hundreds of millions of dormant users that are coming back and it’s actually worth spending some time making sure that that resurrected, for lack of a better word, experience inside the product is really excellent and that you find novel ways to try to bring them back.

Duolingo as an example, they did a good job of using social notifications. And so if people would use contact sync or something, you might get a push notification that one of your best friends just started using Duolingo and that might encourage you to come back and resurrect into the product. And whether you resurrected in the product, it might be the case that your proficiency of the language you were learning, you were learning French three years ago, but now you for forgot most of it. And so when you open the app again, it encourages you to essentially replace yourself, do another placement test and put you in the right spot. And so some of these types of mechanics for a more mature company can lead to pretty good ROI guess is what I’m trying to say.

Lenny Rachitsky: Got it. Essentially, so many people have already tried in the past that to grow, you need to resurrect people that have been there. And so thinking through, it’s almost like a user experience for resurrected users.

Albert Cheng: Exactly.

Lenny Rachitsky: Okay. Let’s zoom out a little bit. You’ve worked at three of the most successful consumer subscription products in the world. What is the difference between how these three operate? I think there’s many ways to be successful. It feels like these companies are very different. What’s the gist of each of these, how they operate?

Albert Cheng: Well, first of all, there’s obviously a lot of similarities, but I’ll just focus my answer on the differences. So I think Duolingo, what struck me most working there is they’re very particular, they have an approach of product development that is infused across everyone in the company. And they actually wrote a playbook about this. It’s called the Green Machine if you look it up. That was one of my most successful tweets ever really.

Lenny Rachitsky: I just tweeted something about Duolingo just released their playbook and I screenshotted the owl’s butt and screened like a page and it was like 5,000 likes.

Albert Cheng: That’s hilarious.

Lenny Rachitsky: Yeah. So yeah, keep going. Sorry.

Albert Cheng: But yeah, the ethos of the company. They hire a lot of intelligent, energetic people out of college basically, and they give them a lot of amazing experimentation, tooling, and they care a lot about the clock speed of the company. So it’s a lot of creativity, a lot of ideation.

The product experience of dual legal actually changes multiple times per day for each user, which is pretty shocking. And so I’d never worked in a place like that before, but it really struck me about how consistently the company operated and they had specs and processes for doing each of those steps in their product development cycle and they were really, really tight about it.

Lenny Rachitsky: Okay, so that’s still lingo.

Albert Cheng: Yeah, that’s still lingo. Grammarly. This is an interesting company because they started as a paid product oriented at students. Then they expanded into more of a freemium model tailored to everyone gradually focusing more on the professional base. And then as they accumulate a lot more professionals, they realize, “Hey, there’s patterns.” We’re seeing that a bunch of marketing teams or a bunch of sales teams or a bunch of customer support teams or whatever, particular functions within particular companies were really adopting Grammarly at scale.

And so they were able to then layer on much more of a managed enterprisey motion. And while I was there, I was focused on the consumer self-serve motion, but they weren’t siloed. They were intermixed with each other. And so a big part of my job was not just to grow the self-serve revenue and self-serve active users, but it was also how do you uncover the right teams, the right functions, the right companies for demand gen and sales to go reach out to?

So that was a very interesting, it’s a product-led sales work, and it’s really fascinating thing for me to learn. And then on top of that, with all the transformation going on with generative AI, and even recently with them acquiring CODA and Superhuman and becoming more of a productivity suite, the company is just evolving pretty rapidly. It’s a really exciting thing for me to be a part of and to see from the sidelines, but that just made it at its core of a different growth job than Duolingo for sure.

Lenny Rachitsky: Essentially a B2B business versus a very consumer business?

Albert Cheng: Yeah, and a lot more meaningful strategic decisions as well.

Lenny Rachitsky: Mm-hmm.

Albert Cheng: And then the core product team also, I’m used to in growth, laying out the entire user journey that a user go through acquisition, activation, engagement, so on and so forth. And typically, growth teams, if they’re well-resourced, they can do enough to move each one of these various levers. And it’s just a matter of the sequencing of them and what you want to prioritize first. But Grammarly was unique in that the core product experience itself was what drove repeated activity.

It’s that I previously mentioned that current user retention thing, what most drives that is the frequency and the quality of the suggestions that you get every day. And so it was an interesting learning in that I staffed up a growth team, tried to work on this metric, and then I realized actually I’m just getting in the way. This is really a thing that the core product team most influences. Let me have a conversation with the core product leader and then shift that over to them. So yeah, just a super interesting experience.

Lenny Rachitsky: And then Chess.com.

Albert Cheng: The thing that’s most unique about chess.com is that they’re super fanatical about chess.

Lenny Rachitsky: Makes sense.

Albert Cheng: Crazy. You shouldn’t be surprised. Obviously the name of the company is like this, but they’ve always hired people from around the world. The company’s always been globally remote. They just hire people that love chess. They play all day, they watch the streams. Our Slack is always blowing up with people’s chess moves and games and whatnot. I think I want to say this a little bit delicately, like Duolingo, even though the product they’re providing is around language learning, I think the original ethos of how to start the company was really around motivation.

The hardest thing to its habits, it’s how do you build that daily habit? And I actually in many ways see language learning as their first vehicle. And what they have a superpower in is that, again, the motivation, the habits, et cetera. So that’s Duolingo, and Grammarly actually similarly. People know them for the spelling and grammar corrections, but what’s really unique about them is they’re integrated across tons and tons and tons of applications.

There’s not many products that work like that, that’s really unique. And so now if you hear Shishir, their new CEO talk about the AI super highway and all that type of stuff, they can now use that technology to provide a lot more than just grammar writing. And so my point is just that Chess is about chess 100%. It’s in the ethos. People are crazy passionate. That just means we’re always dogfooding the product. There’s just an amazing energy in the company to just use the product all the time, come up with ideas, and I love that environment. I think that’s fun for me.

Lenny Rachitsky: That is so cool. What I love about what you’re saying is there’s no right or wrong answer. All of these companies are killing it. I think Duolingo is worth like $10 billion, something like that, and keeps growing. I’ll look it up in a second. And Grammarly is worth a ton, and then Chess.com is doing super well. So I think that’s a really interesting takeaway here is you can succeed in a lot of different ways.

Albert Cheng: Yeah.

Lenny Rachitsky: What’s really cool about Duolingo, I was just thinking as you were talking, is yeah, it’s just interesting that this very structured, methodical way of building is working so well because you could listen to that and be like, “Oh, I don’t want to work.” This is rigid way. But the fact that it is killing, it tells us this actually works really well. If you find something that works, lead into it.

Albert Cheng: Yeah, that’s right. Yeah, the structure is rigid, but the ideas are the farthest away from rigid as possible. You have seen their, I don’t know, Superbowl commercials, they’re memes, gamification, tactics. It’s a super fun creative environment. So rigid is the farthest possible word to use, but what I just mean is they’re consistent. They have for everything, and their product reviews are 10 or 15 minutes. It’s just people go in and out. So it’s just this kind of a surreal environment about how rapidly and consistently they work.

Lenny Rachitsky: Awesome. They’re worth $12 billion, and they were much higher actually, not too long ago. They’re coming down a little bit. So speaking of Duolingo, when people think Duolingo, they think of the brand and the owl and the success they had on TikTok and things like that. I’m curious to get your take on as a very growth-oriented person watching that work and your take on growth, experimentation data versus marketing, viral TikTok videos, mascots, things like that.

Albert Cheng: Yeah, I used to think it was versus, but now I realize that they combine really well. It could be rocket fuel for your growth. Yeah, being a product person. I joined a lot of these companies literally on the home screen on my phone, and I like using them. And I consider myself someone that’s not easily swayed by ads or TV commercials telling me what to buy.

So I always had an element of skepticism on the marketing side for much of my career. But then, yeah, you join a place like Duolingo and you see how Duo the owl has developed a personality through the push notifications and the product experience, and then seeing the marketing team leverage that personality in their TikTok and in their YouTube and all throughout social media and just feed into those memes. And then we would track back in the product experience, how did you hear about us?

And put all those channels in there. And some days, it would be like, holy, it’s bringing in 20, 30% of our new users and any given day. So those two things really go hand in hand, and that feeling has only been reinforced by Chess.com over the last five years. The first 15-ish years of this company was really under the radar. 800 million people play chess around the world, but most of that is over the board.

Until recently, there wasn’t actually that much online, but five years ago, everything changed. You had the pandemic, you had Queen’s Gambit, you had a lot of YouTube and Twitch streamers, you had a bunch of kids playing it in school, et cetera. And so it’s really the combination of those two things that make it take off. And it’s like the growth experimentation is more the slow and steady or fast and steady, I should say, approach where you’re just continually iterating, you’re making the product experience better, but then every so often, there’s a big wave that comes in. You can quadruple your registrations overnight and you’d be a fool not to take advantage of that.

Lenny Rachitsky: I was actually speaking at Chess.com and playing chess. I was at a coffee shop this weekend. There’s a family, a dad and mom and a daughter ordering, and the dad’s sitting at the table and he’s just on his phone, just opened up Chess.com secretly and just plain while he is waiting. Oh man.

Albert Cheng: I will not admit or deny that I’ve done that before.

Lenny Rachitsky: But if I can think of anything more wholesome, I can’t. That’s an amazing thing to be doing while you’re just sitting.

Albert Cheng: My 4-year-old can actually set up the pieces, which is pretty great. So he enjoys the game quite a bit.

Lenny Rachitsky: Oh man, this 4-year-old already a pianist, playing chess.

Albert Cheng: That’s right.

Lenny Rachitsky:

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I want to follow that thread. As a growth person, imagine AI informs chess.com in a lot of ways, so there’s kind of two buckets here. How is AI changing the product, say chess and other places you’ve worked? And then how is AI impacting your work as a growth person? So pick one or both buckets and share there.

Albert Cheng: Yeah, I’ll tackle them in sequence. I’ll start with the chess one just because I have maybe a slightly unique take on that one. So chess and AI, they’ve been intertwined for almost a century. Some of the early computing pioneers, they just figured, “Yeah, chess is an interesting game. We can test machine intelligence and write some algorithms or not.” And then fast-forward to 1997, and you had IBM, they had their DeepBlue application who actually beat the world champion back then, which was Garry Kasparov.

And that was a huge moment of shock and reckoning of like, “Oh man, is AI going to take over? Humans are, we’re going to have jobs and all this stuff.” And this is 30 years ago, and thankfully we’re all still here and more people are playing chess than ever. And so the game of chess and chess.com specifically have learned how to augment, I guess the human playing experience with the power of chess engines, which are definitely a powerful form of AI. It’s not LLMs to be clear, but there’s engines like Stockfish these days that are just dramatically better than the top grand masters in the world.

Lenny Rachitsky: Is that where we’re at? I remember when it beat humans and now it’s just dramatically better.

Albert Cheng: It’s dramatically better.

Lenny Rachitsky: Wow.

Albert Cheng: Yeah, I think there’s a rating system that compares relative skill level and an average chess player somewhere like a thousand, maybe 1,500 on the high end, a top grandmaster like Magnus Carlsen, it’s like a 2,800 and then Stockfish and similar engines are like 3,600.

Lenny Rachitsky: Wow.

Albert Cheng: And so to put that in comparison, yeah.

Lenny Rachitsky: At least it’s not 10,000 or a million. I don’t even know if that’s possible.

Albert Cheng: No, it’s not 10,000. But it’s similar to if the chess engine was playing without a major piece like a rook or something, they would still be competitive against the best players.

Lenny Rachitsky: And this is the Elo score? Is that the term?

Albert Cheng: Yeah, the Elo score, Elo rating.

Lenny Rachitsky: Magnus is what you said about 2,800, and then the Stockfish is would you say 3,600?

Albert Cheng: Yeah, and really it’s because computing power is so amazing and there’s so many techniques for how to do deep evaluation on specific chess lines. They can calculate tens of millions per second. So it’s not realistic for a human to compete against that. But yet, watching some of these chess engines played has opened up a lot of creativity, new strategies, new lines, new appreciation for the game. And our chess.com approach is that we can bring this technology for every user.

Even people that have never moved a piece before. I talked earlier about that game review product, that’s exactly what this does. So behind the scenes, we’re running chess engines to basically spit out evaluations for every move that you make. And then we translate that and make that approachable to the user using their native language and plain approachable style, and even with audio and things like that as well. And that part of it, the personality, the speech back to the user, that part is LLMs.

And so I guess my point is that, again, chess and AI have been intertwined forever, but for us, what’s most important is that we keep the customer at the North Star of it. We’re not just applying LLMs just because the new hot thing, you’ve got to apply the right technology for the right feature to provide value to the user. And so we try not to ever lose sight of that and let hype get us too carried away.

Lenny Rachitsky: It’s just really surprising. I think people would not have expected AI and cannot beat every human alive ever. And chess is at an all-time high. People want to keep playing and are playing more and more than ever played, not unexpected.

Albert Cheng: Interestingly, LLMs themselves are quite bad at playing chess. They hallucinate moves, they look at patterns. They’re very good at pattern recognition, but not so good at going super, super, super deep on a specific chest thing. And if you’ve even tried to create or look at chessboard images on ChatGPT, a lot of them have the wrong number of squares. They’re not set up properly, and so I don’t want to be too dismissive.

I’m sure it’s going to get much stronger at reasoning. And actually, Google recently sponsored a tournament where all the top LLMs played a tournament against each other. So that was pretty fun to watch. They’re improving, but chess is specifically a game that having a trained deep, deep computing engine is just going to be much, much, much more powerful than LLMs.

Lenny Rachitsky: And not to go down this track too far, but AlphaZero famous for beating the Top Go player. Was that trained specifically for Go? Obviously not in LLM, but that was a Go specific model.

Albert Cheng: Yeah. My understanding is that the one, that documentary is incredible, by the way. I don’t know if you’ve watched AlphaGo, it’s amazing how they took something so technically deep and made it so emotional and human. But I think that’s the crux of how we feel, I guess, about AIs and the products that we build, actually. But to your point, my understanding is that the way AlphaZero is primarily trained is that it just plays a bunch of games against itself. And so through the neural network, it just gets smarter every time. And because it can have that repetition times a billion or a trillion, I don’t know exactly what number, but it’s going to get pretty damn good.

Lenny Rachitsky: Okay. Let’s go back on track to where we were going. So this was how AI is impacting chess.com. How is AI changing just the work of a growth person?

Albert Cheng: I like to describe growth as the job is to connect users to the value of your product. And in order to do that, what I like to do is think about that user journey again, and essentially, staff teams that are oriented around each element of that user journey. And those teams have specific metric goals, they have roadmaps, et cetera. And then they go run against them.

So that’s how it’s structured. AI, I think can be applied to speed up some elements of that essentially experiment cycle that you get through. So one example is in product discovery. As opposed to core product, which tends to have longer timeframes, and you might do thorough user research or market research. It’s more foundational, more for first principles, et cetera. Growth is a little bit less like that.

It’s like you’re running a lot of experiments and the output of any given experiment is the input to your next idea. And so historically, I don’t even mean historically, but just a few months ago, we were operating in a, that’s history, I suppose, but there would be a lot of manual writing of these analysis docs. You’d have to read them, you’d have to understand what insight you want to grab from them and then write another spec to translate that idea. That’s still happening to some degree, but I think that’s a spot where even tools like ChatGPT are super helpful.

You can just plug in like an analysis that another person wrote and just have it summarized for you and give you advice on ideas to go try. And so that ideation, that research cycle was much, much faster. I talked a little bit about prototyping also just becoming much, much faster than before. We have not yet gotten to the point where product managers themselves are actually shipping the code into production, but it’s dramatically shortened the amount of time it takes to conceive of especially a bolder idea that you might have.

And so when I talked earlier about explore and exploit, a lot of the explore was harder to do, but now it’s a little bit easier to do. You can take a broader concept and visualize it, and when you can visualize it, send it around the team, get people to click around it, that makes a world of difference. So those are just a couple examples that come to mind.

Lenny Rachitsky: Awesome. I want to go back to this phrase right at the beginning of this answer that you shared that I think is really helpful that you see growth as simply your job is to connect users to the value of your product.

Albert Cheng: Yeah.

Lenny Rachitsky: Can you speak more to that? Because I think that’s such a nice way clarifying what is growth’s role?

Albert Cheng: Yeah, it resonates deeply with me because I feel like growth sometimes gets this reputation I guess that it’s just pure metrics hacking, like we’re cold people that just are trying to move a particular metric up and we’re going to do whatever it can to throw walls and pay walls and add friction in all these spots. And even though that could theoretically work at a micro level on a specific feature or a specific metric, I think what’s most healthy for a company, and I want to work at durable companies is to think about the user holistically.

And when you take that framing of connecting users to the value of your product, that value can change for a user over time, and that also lines up really nicely to the journey. What someone that’s not even a user yet needs to understand about the value proposition is super different than what a habitual user of three plus years might need. And so the teams working on them should think from that perspective and then from there, then ladder into specific problems to solve hypotheses, et cetera.

Lenny Rachitsky: Following that thread a little bit more, people listening to this are imagining, “How do I get better at experimentation? How do I run more experiments? How do we do this better?” What are two or three tips and best practices that you think people need to hear maybe are not totally aware of when they think about getting better at experimentation on our teams?

Albert Cheng: I think the first thing is just start somewhere. I just read this Atlassian state of product report and it was like 40% of product teams basically don’t run experimentation at all. And there may be some good reasons for it. It could be philosophical or maybe you’re more B2B oriented or whatever. So I get it, but I think for a lot of, especially if you work on a consumer product that has some degree of scale, some degree of frequency with your product, you can collect enough data.

And also I have found I can pattern match all day long. I’ve worked a lot of companies, right? But I’m wrong all the time. And I think consumer behavior can be very fickle and especially when you work at a company, you become a power user naturally. So sometimes you may forget what the actual user experience is for a brand new user, and so you leave a lot of opportunities on the table if you don’t even try to experiment.

So I just encourage taking that first step, just run an A/B test, find a third-party tool or something that you can integrate quickly or even just work with your engineers to spin something up. Just get in the practice of crawl then walk then run type of thing.

Lenny Rachitsky: Do you have a favorite tool, by the way? Just to throw out? Is there a go-to tool for you?

Albert Cheng: We used Statsig at Grammarly and I saw that they recently got acquired, so that was exciting news. Duolingo and Chess.com both have an in-house experimentation approach.

Lenny Rachitsky: Sweet.

Albert Cheng: Pros and cons to either. Obviously Duolingo is an experimentation machine, and so it’s been a huge accelerant to have our own thing specifically tailored to be excellent at that. But no, I typically don’t encourage companies to build experimentation in-house from day one. At a certain scale it can make sense. And some of these companies, they were started 15 years ago when these tools weren’t out. So it was just something they had to do.

Lenny Rachitsky: Something that you mentioned to me at Chess.com, your goal is to run a thousand experiments a year. You said you were at 250. Talk about just that as a North Star.

Albert Cheng: Yeah, so part of having team members that are fanatical about Chess is that the company can get pretty far just building for themselves, building for the community, and not actually being very experimentation and data oriented. The problem with that is that you can have relatively lumpy growth. And so part of the excitement of me joining the company was to help smooth that out and bring in that experimentation mindset.

So prior to 2023, the company practically didn’t experiment at all. Last year they did about 50, this year they’re on pace for about 250. And then next year we have that ambitious target of a thousand. Did I make it up? Yes, absolutely, I made it up, but it’s still a target and a thing for the teams to think about and a thousand experiments by itself. If you just did that but you didn’t learn, you didn’t make an impact, that’s kind of a waste of time.

The whole point of setting a goal is that you can have conversations about what would need to be true to actually hit that goal, and so that leads to insights. Actually we need not just product management or engineering to be running these experiments. We can experiment with lifecycle marketing, changing copy of push notifications and emails. We can experiment with app store screenshots and keywords and stuff like that. We have all sorts of content marketing teams, et cetera. We could have engineering enable no code for specific screens.

Think about our home screen or our pricing screen where we might want to do a lot of just tests that are configurable without engineering support. We might want to just track our progress and look at it from time to time and make sure that we have the right observability around this. So anyway, that’s the stuff that really matters as opposed to the hitting that goal itself. So don’t tell the team, but I don’t actually care that much if we actually hit a thousand, but I think if we get pretty close and we accomplish some of these things, we’ll be in really good shape.

Lenny Rachitsky: Okay, we’ll make sure none of them watch this. I think chess.com is in, this is just such a cool example of a culture shifting dramatically from zero experiments to sounds like two years later, a thousand, which is three a day. There’s many teams running experiments in parallel, but that’s a lot. What has helped you most shift that culture? Is it just the CEO being like, “This is the way we’re going to go.” What have you learned about helping shift to culture from No, we’re not doing experiments to a thousand experiments a year.

Albert Cheng: Yeah, definitely a lot of credit to the CEO and co-founders like Erik and Danny, they’re amazing. It’s not their intuitive way of thinking about growing companies, but their mental flexibility and encouragement to evolve and add this as a tool for the company has been awesome, and they’ve been on the front lines preaching product-led growth and experimentation just as much as I have.

So I’m glad that you brought that up because I think that is critically important for me, joining a company to not be at odds with the co-founders and the existing approach of the company. I think that’s absolutely, absolutely critical. I think I started this podcast with the example of the game review and the positivity and how that was shared. I think those types of things are really what motivate people. They need to see this working in practice.

Lenny Rachitsky: Wins.

Albert Cheng: Yeah, you need wins, you got to celebrate them. People feel good about the learning. It’s applied across the board. Who’s not going to be energized by that, I think, right? So you can’t just set goals in a vacuum and create it from top, right? People have to see it working and when it works, the metrics move and you learn faster and you ship faster, and that’s a great environment to be part of.

Lenny Rachitsky: What was the first experiment you guys ran? Do you remember?

Albert Cheng: I don’t know, before my time actually.

Lenny Rachitsky: Okay. Okay. Got it. So they’re already going down this track before they brought you in?

Albert Cheng: They had run some.

Lenny Rachitsky: Okay, sweet. Are there any other key lessons that you think people need to know to be successful running experiments at scale?

Albert Cheng: The system matters just as much as any given experiment, probably even more, right? I think starting with a growth model, so you have an understanding of how your company grows in the first place and which channels you’re going to leverage is critical. You need to make sure that you are instrumenting your product in and out. Otherwise, you’re going to run experiments and have wonky results.

I won’t name which company, but I was part of a company that had an in-house experimentation tool. It’s about three months into the company, we’re running some experiments and we realized that user retention was actually configured backwards. So all positive results were negative results.

Lenny Rachitsky: Geez.

Albert Cheng: So that was kind of embarrassing and that will never happen again.

Lenny Rachitsky: You just go through and undo all those experiments and just drive up retention.

Albert Cheng: It’s kind of weird. We’re seeing people use the features a lot more. Why is user retention going negative? So I have plenty of horror stories around that type of stuff, but yeah.

Lenny Rachitsky: Oh my God. On the flip side of horror stories, you’ve shared a bunch of cool examples of experiment wins. Is there another that comes to mind of one you’re really proud of or that was really trajectory changing either at Duolingo or Grammarly or Chess?

Albert Cheng: So I already shared one of Chess.com and one of Grammarly. I could talk a bit about Duolingo as well. Duolingo and you had Jackson on the podcast, right? Where you talked about the streaks.

Lenny Rachitsky: Yes, talked about the streaks.

Albert Cheng: So I also don’t want to steal his thunder because I was going to think about that, but the amount of learning through commitment and putting streaks on a calendar and just getting people started as opposed to achieving some large milestone, that was huge. I think we did something interesting. We spun up a virality team and virality is this really amorphous thing to me.

I think it’s really hard to generate virality in your product, but Duolingo is a product that is shared quite a bit. And so we invested actually in some time to essentially add screenshot tracking for a brief period of time in the app just so we could find out the hotspots of where users were doing screenshots. And you see this in other apps too, it’s not necessarily some horrible thing, but we did this for some period of time and we were able to basically articulate and say, “Okay, streak milestones is the obvious one.”

Really funny challenges that you get in the Duolingo experience is also super highly shared. Advancing in the top three of a leaderboard is another thing. Anyway, so you can find these different moments where that’s the case. And then we staffed those moments with illustrators and animators and created these really delightful experiences around them, and that worked amazingly well.

So as opposed to going against I guess human intuition and trying to get them to share stuff that they otherwise wouldn’t on the margins want to share, lean into it more, actually grab the moments where users are already organically screenshotting and make those much, much, much better. And you can 5X or 10X and drive a lot of growth that way too. So that’s not so much an experiment, it’s more a core product thing, but it just resonated with me that that was interesting.

Lenny Rachitsky: Well, it connects to your explore and exploit methodology. Just find or explore where things are happening and then try to exploit in a nice positive way.

Albert Cheng: You got it.

Lenny Rachitsky: Speaking of that, you mentioned this with Duolingo is just very good at habit formation and motivation behavior. It feels like chess is good at this too. You’ve worked at both these companies. What have you learned about how to motivate people? How to create habits?

Albert Cheng: Again, Duolingo would not have started without this insight from day one. They aim to focus on motivation and build a lot of these tactics. Jorge actually had this model of gamification patterns having essentially three pillars to it. You have the core loop, you have the metagame, and then you have the profile. And so we actually thought about it that way too, where your core loop is your lesson that you go through. You do a lesson, you get some rewards, you extend your streak, and then the next day you get a push notification.

It’s the core loop of the product and making that really tight is super important because people need a habit to stick to. Then you need a metagame, which for Duolingo is the path, but it’s also the leaderboard achievements. It’s long-term things that you’re going to strive to such that you have long-term, I guess, motivation to continue doing the thing. And then the profile is also critical because you build up a profile over time.

It’s a reflection of your investment inside the product experience. And so when you nail those three things, you can end up with a long-term learning journey that can be quite successful. And then to flip over to the Chess.com side, what we see is that over 75% of our new users, they classify themselves as like, “I’m completely new to chess.” Or, “I’m a beginner.” And unfortunately, if you’re new to chess and you’re a beginner, you’re not going to have that fun of a time playing live games, and we see this in the data. It’s like less than a third of those users actually win their first game. And when you lose a game, user retention is 10% worse than when you win a game.

Lenny Rachitsky: That’s not so bad, but at scale, that’s bad.

Albert Cheng: Yeah, and it could be worse. That’s true. And so typically what a lot of mobile games will do is they’ll just create a super simplified version of the game. It’s harder for us to do at chess, and so without changing the rules of that, I think that’s, I don’t know, it’s just very eye-opening to me when you’re trying to learn something, whether that be language learning or chess or whatever, usually those first steps are fraught with a lot of self-doubt and reinforcement that you’re not good at the thing. And so it pays to be very intentional to craft experiences that guide the user around that.

Lenny Rachitsky: Well, I can’t help but ask, is there anything that helped that along?

Albert Cheng: Yeah, so something we’re experimenting right now is just like purely if you say that you’re new to chess, we’re going to craft a more delightful learn how to play experience as opposed to dropping into a live game, that’s an example. Another is hiding your ratings for the first five times such that you’re not seeing your rating plummet. So there’s a lot of tips and tricks you can do.

Lenny Rachitsky: I’m just imagining a little guide that’s like, “Here’s how you win.”

Albert Cheng: Yeah, or play against a coach, play against a friend, play against a bot. There’s a bunch of different avenues you could take.

Lenny Rachitsky: Well, what I’d love is play against someone real and here’s where you should move. Just like, “Hey, here’s we’re going to help you win.”

Albert Cheng: Like a hint in real-time?

Lenny Rachitsky: Yeah, yeah, yeah.

Albert Cheng: I don’t want to be playing with you then.

Lenny Rachitsky: Okay. Let me ask you a couple more questions. One is just zooming out a little bit, what’s the most counterintuitive lesson you’ve learned about building products or building teams across the many companies you’ve worked at?

Albert Cheng: Yeah, I’ve talked a lot about products. So maybe I’ll flip to the team side for a bit. I think the standard way to hire and build a team is you fill out a JD, it’s got a whole bunch of different characteristics that you’re looking for. You typically will find a short list of companies that are kind of similar to yours, and then you try to hire for that, right? I think that’s the typical default path that a lot of companies take.

And I was really struck by my experience working at some smaller startups or take Duolingo as an example, where over and over and over, I saw some of the highest performers just being people that had very high agency, had that clock speed, had that energy. Yes, they cared about the mission, but they didn’t necessarily need to have deep experience on that matter. And in fact, sometimes that experience could be a crutch in certain ways, especially in this world where the grounds are shifting so fast with AI, a lot of your learned habits actually need to be intentionally discarded.

You need to have a beginner’s mind on this type of stuff. So I think this is more true than ever, looking for people that respond and move quickly and think just faster and move faster. I think the fastest speed of learning, those types of companies are the ones that I want to bet on. I think those will end up surviving and thriving.

Lenny Rachitsky: So just to double click on this idea of high agency is very trending these days of just higher high agency people. To unpack that a little bit, you mentioned a few of these traits, so let’s just help people see what you see. So one is clock speed, just they think fast, they move fast, they learn fast. What else? What else do you look for that helps you see that there are high agency people?

Albert Cheng: Yeah, a lot of it actually happens outside of the interview process interestingly. So a lot of it is the types of questions they asked, “Have they actually tried your product and gone deep into it?” A lot of it is, it’s the references, it’s the communication that they have to even set up your interview, it’s the energy they bring into the conversation.

You can actually pick up a lot of soft signals on some of these traits over time. You’ve got to pick up on some of these patterns. I don’t know that I’m perfect at it, but I’ve learned to balance those things quite a bit more than I did in the past when I would just purely read from my questions and my rubric and not care about anything else.

Lenny Rachitsky: Yeah, there’s like a vibes component to it. This is also support for the work trial way of interviewing versus just a talk interview where you have them actually work with you for a week or whatever.

Albert Cheng: That’s a great point.

Lenny Rachitsky: Okay. One other question I wanted to ask you. You’ve worked at a bunch of different sizes of companies from startup to Grammarly, I don’t know, you call it a big company, bigger company. Duolingo, I don’t know how big is Duolingo?

Albert Cheng: There are about a thousand people.

Lenny Rachitsky: Okay, cool.

Albert Cheng: But I worked at Google too to start my career.

Lenny Rachitsky: Oh, right, okay. What have you learned about just the size of company that makes you happy? What have you learned about just helping other people that you talk to decide what size of company is good for them?

Albert Cheng: I definitely believe that everyone has a company stage that they shine best at. I’ve personally gone through this journey of big tech to tiny, tiny, tiny startup, then landed in the middle, which I consider my own goal lock zone. I talked earlier about what actually gives me personally a lot of energy is seeing across a company’s efforts, but also the company being small enough that I can get into the details, I can work with the specific teams.

I can read experiment results, I can look at the pixels. And so I find that the balance of those two things tends to fit best with medium-sized companies, but that’s me, right? I think at big companies like a Google, you’re dealing with immense scale, which is interesting by itself. You learn a lot of best practices from your peers. They have all the tools and functions that you would possibly want to go learn from, but they can tend to move slower and it’s harder to ship things and get them out the door, which eventually drove me nuts a little bit.

On the flip end of the spectrum, these tiny startups, they move incredibly fast, but I grew all my gray hair from those tiny startups because no one knows about your company, and so you’re recruiting people one by one. You’re trying to get users one by one. So yeah, you can learn fast and ship a lot of things, but if you’re trying to make a big impact on the world, it can be actually pretty grueling to do so at really, really, really small startups.

Now, some of them do hyperscale and make it out, and obviously, I am not one to trash that because the path that I tried for quite a while. But for me, I really like the zone where I can contribute at scale, but also execute at a pace that’s more on the daily and weekly scale as opposed to monthly and quarterly.

Lenny Rachitsky: And when you say medium, what size of company is that roughly?

Albert Cheng: Yeah, so these companies that we’ve talked about in the podcast are about 500 to a thousand people. Typically, these companies who have been around let’s say 10 to 20 years. They’re durable, ideally profitable, have a good leadership team, but there’s still a lot of dimensions to go figure out. A lot of them are in key inflection points, so they’re certainly not stagnant. You need to find a place that’s dynamic too.

Lenny Rachitsky: Interesting, 10 to 20 years old, I don’t know if that’s a, not many people would feel like that’s where I want to be. I love that you found a number of companies like that that you enjoyed working at. The last question, and this is going to be taking us to a recurring segment on the podcast that I call Failed Corner.

People hear all these stories of all these experiments and all these companies that worked at, they’re all killing it up into the right. In reality, you’ve touched on this, a lot of things don’t work out great. So can you share a story when something went wrong, when you failed and what that taught you?

Albert Cheng: First of all, in the growth world, you’re failing all the time. So I’m not going to pick a specific growth story because those don’t actually hit my ego too much. But earlier in my career I did a lot of core product work. I worked for this startup called Chariot. I don’t know if you ever lived in San Francisco, but.

Lenny Rachitsky: Yes, it was like the bus super thing.

Albert Cheng: The blue commuter shuttles, like 15-person shuttles, they would essentially drive from various neighborhoods into downtown San Francisco. It’s a commuting use case across between the public bus system and an Uber and Lyft. So I was there for some time. I led product there and the core service was really loved by its users. It was reliable and fast and affordable enough, but we got pretty interested in this idea that maybe we can improve utilization, maybe we can make the service a little bit more innovative if we offer dynamic routes more similar to Uber and Lyft.

How could the drivers are driving these fixed routes? But if they have spare time, they can go out of their way, go pick up somebody at their house or something and keep going. So we tried this, we called the chair direct, really interesting attempt, but I learned a lot of lessons there because ultimately it didn’t work out. One lesson is like this was kind of a solution searching for a problem. You never just purely want to chase A, it wouldn’t it be nice if we did this as opposed to this is our user and this is the problem that we’re solving, this is why it’s going to delight them, et cetera, that’s one.

Second is you got to consider, especially in these more marketplace type businesses, there’s more than just one end user and we focus so much of our attention on the writer app without realizing, oh yeah, the drivers are carrying a lot of the brunt of this experience and our operations team is as well. And so when the drivers are confused or disgruntled, that can lead to a challenging overall experience for the product. So that’s definitely another one.

And the third one is we did a lot of actually PR, prior to the service going out just to get the word out. And PR has its time in place, but I think doing it before you have validation that customers definitely want, the thing is quite risky and it can lead to a lot of sun cost once you get it out because you need to see it through, you want to see it succeed. So yeah, this is a decade ago, honestly, I had a great time at that company, but I still remember that vividly because it contained three or more key lessons that carried forward as I have built many products since then.

Lenny Rachitsky: Yeah, it feels like you went to the complete other end run experiments of everything before you tell anyone about it.

Albert Cheng: That’s right.

Lenny Rachitsky: Yeah, I remember the chariot bus showing up at the Airbnb office and people getting, I’m like, “What the hell is this?”

Albert Cheng: That’s right.

Lenny Rachitsky: Very cool. I didn’t know you worked there. Albert, we’ve covered so much ground everything I was hoping we’d cover. Is there anything else that you wanted to cover, anything else you want to leave listeners with before we get to a very exciting lightning round?

Albert Cheng: No, this is great. I hope it was useful for your listeners. I will say over the last few days, as I was prepping for this, I was honestly a little bit anxious about do I have enough deep independent frameworks that I need to come up with? But just being authentic to my actual experience at these companies, a lot of my lessons learned have been off of the backs of other people that have tried similar things and have succeeded or failed.

And I think what’s important is that you have that your mental sponge. You can try a bunch of different things, you can absorb them and then put them in practice right away, discard the things that don’t work and evolve them for yourself and for the company’s needs. And so I don’t know, I think that was just a realization that I had as I was thinking through this podcast, and I think that’s partly why I haven’t done too much public speaking.

Lenny Rachitsky: I know exactly what you mean. When I left Airbnb, I was just like, and that was the first time I ever took a break in my career of 30 years of just working straight in school. I was just like, what have I actually learned? I’ve never just sat down and thought about, here’s the thing I’ve learned. And that led me to writing this medium post that did really well what I learned at Airbnb, and then that basically led to what I do now. So there’s a lot of power and I love that this is the excuse to make you think through what have I learned concretely that I can share.

Albert Cheng: That’s right. Thank you for that.

Lenny Rachitsky: Yeah, and so at the beginning of this podcast, before I started recording, I always like to ask guests, what is your goal? What do you want to get out of this conversation? And usually, it’s like we’re hiring. We want to make sure people know about our company or we want to get the users. And your answer is just, I just want to give back things I’ve learned, which I love.

Albert Cheng: That’s it.

Lenny Rachitsky: And you’ve done that. With that, we’ve reached our very exciting lightning round. I’ve got five questions for you. Are you ready?

Albert Cheng: I’m ready.

Lenny Rachitsky: What are two or three books that you find yourself recommending most to other people?

Albert Cheng: Yeah, so the truth of it is I have a, not just the four-year-old, but I also have a one-year-old. So most of the books that I’m reading these days are kids’ books, trying to make them laugh in all.

Lenny Rachitsky: Wait, any favorite kids books? Because I have three or two year olds already.

Albert Cheng: Well, you said that you started singing. There’s a book called Snuggle Puppy that has a song in it that just makes my daughter crack up.

Lenny Rachitsky: Oh my God.

Albert Cheng: That is heartwarming for me. But no, a book that I recommended recently at work is Ogilvy on Advertising. Do you know this book?

Lenny Rachitsky: I don’t know the book. I’ve seen these tenants of marketing or whatever.

Albert Cheng: Yeah, it’s interesting. So it’s 40 years old, but it’s just packed with a bunch of different practical examples about copy and creative that work in, these are old school ads, but he took a very experimentation-oriented approach to just try a lot of things.

I think in the book, it makes a good reminder that what ultimately matters is to compel your users to some action for him as buying a product, right? It’s not about just creating clever ads or sexy creatives, it’s to do things that compel that action. I think that’s very true for many of our product and life cycle teams. And so I shared that around as an interesting recommendation.

Lenny Rachitsky: Is there a movie or TV show? Sorry, were you going to share another book?

Albert Cheng: Yeah, actually.

Lenny Rachitsky: Oh yes, please.

Albert Cheng: Our co-founder at Chess.com, his name’s Danny Rensch, and he is quite well known in the chess circles. He’s releasing a memoir called Dark Squares, and it is super fascinating. He grew up in an abusive cult and was a chess prodigy. And so it is just this unbelievable story and I’m about halfway through it, it’s a reminder that sometimes the people that you work with, you don’t realize how deep their pasts go, but this is something else, and I think it should be out by the time this podcast releases.

Lenny Rachitsky: And it’s called Dark Squares?

Albert Cheng: Dark Squares.

Lenny Rachitsky: Which is a reference to the chess board and also I imagine the difficult past.

Albert Cheng: Exactly.

Lenny Rachitsky: Wow. How cool. Okay. Are there movie or TV shows you really enjoyed that you’ve recently watched?

Albert Cheng: These days it’s football season, so I’m consumed by all the hot takes of my favorite teams that I love and the teams I love to hate as well, so.

Lenny Rachitsky: Who’s your team?

Albert Cheng: The 49ers. I have season tickets and I go all the time. We had a rough season last year, so hoping to turn around.

Lenny Rachitsky: Okay, very cool. Okay. Is there a product you’ve recently discovered that you really love?

Albert Cheng: Yeah, so last 20 years of my life roughly, I’ve moved around a lot, but I’ve always been within walking distance of a coffee shop. It’s just like a ritual that I go and get coffee and it starts my day, right? Two years ago, I bought a house and for the first time ever in my life I’m like not buy a coffee shop, and I was so depressed about this for a little while.

So my favorite product is the bread bowl barista, and it just starts my day off. I like making horrible latte art with it, and I think it’s just a reminder. I don’t know. The products that most impact me, I guess are the ones that I use all the time, and it’s a daily habit-

Lenny Rachitsky: And have the most caffeine.

Albert Cheng: Then the most caffeine. You got it.

Lenny Rachitsky: Amazing. Do you have a favorite life motto that you find yourself using in work or in life?

Albert Cheng: As I was thinking about my piano stories, I also remember that my mom used to have a quote. She just said, “Nothing is more important than your reputation.” And she used to say this, and I think the charitable understanding of this is that a lot of the small decisions that you make each day, how do you treat people? How do you show up? What’s your character, et cetera. They can compound and they open doors for you in many surprising and amazing ways.

A lot of these companies that have actually joined have come through relatively light connections. And even just being on this podcast, I think I’ve seen a number of folks that I’ve worked with before beyond the show. And so I think doing the right thing, building a good reputation, they can carry you a long way. And the flip side of that is reputations are fragile too, right? So if you do the wrong thing, take a long time to repair that. So I don’t know, it just stuck with me my entire life. I thought that was an interesting life motto.

Lenny Rachitsky: Last question. You work at Chess.com, how’s your chess?

Albert Cheng: Terrible compared to serious, serious players, but quite compared to the casual ones, yeah. My yellow rating is about 1,800 for a rabbit games.

Lenny Rachitsky: It sounds really-

Albert Cheng: And about 1,500 for blitz. Yeah, but I play many times every day.

Lenny Rachitsky: Blitz is like fast chess?

Albert Cheng: Blitz is like faster chess, three minute games. Rapid is more like a 10-minute game, which is still pretty fast.

Lenny Rachitsky: And you say you play multiple times a day? Do they make time? Is this like-

Albert Cheng: They do.

Lenny Rachitsky: Okay. At Patagonia, there’s a famous book, the founder wrote called Let My People Go Surfing, and the rule at Patagonia is you can go surfing if the waves are great. Is that how it works at Chess.com?

Albert Cheng: Absolutely.

Lenny Rachitsky: Okay.

Albert Cheng: Chess is always fun. So we play all the time and they even have chess coaches on staff.

Lenny Rachitsky: Staff, just like you can book to do?

Albert Cheng: You can book. So I get bi-weekly lessons and it’s helping me improve.

Lenny Rachitsky: Wow. Okay. This is going to drive a lot of hiring for you guys. Saved it for the end. Albert, this was awesome. Thank you so much for doing this. Thanks so much for giving back and sharing all these stories. Two final questions, work and folks find you if they want to follow up on some of this stuff, and how can listeners be useful to you?

Albert Cheng: Yeah, thanks for having me. This was great. You can find me on LinkedIn or Twitter. Not a super active poster, but I read it all the time. If there’s something that I said today that resonates with you and you just want to get in touch, trade notes, feel free to reach out.

Lenny Rachitsky: And can they play with you on, can they find you on Chess.com to play?

Albert Cheng: They can.

Lenny Rachitsky: Okay. Do you want to share your username or you don’t want that?

Albert Cheng: I am happy to.

Lenny Rachitsky: No. Okay.

Albert Cheng: I just mentioned that I’m a 49ers fan, so my username is Go9ers, so.

Lenny Rachitsky: Wow.

Albert Cheng: I’m sure I’ll get a lot of game requests now.

Lenny Rachitsky: Here we go. Here we go. 1,800. Okay. Albert, thank you so much for being here.

Albert Cheng: Yeah, thank you so much.

Lenny Rachitsky: Bye everyone. Thank you so much for listening. If you found this valuable, you can subscribe to the show on Apple Podcast, Spotify, or your favorite podcast app. Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast. You can find all past episodes or learn more about the show at lennyspodcast.com. See you in the next episode.

Glossary

English	中文
agency	自主性
aha moment	顿悟时刻（aha moment）
B2C2B	B2C2B（消费者到企业商业模式）
Brian Balfour	Brian Balfour（增长领域专家，Reforge 创始人）
Chariot	Chariot（旧金山通勤班车创业公司）
Chariot Direct	Chariot Direct（Chariot 的动态路线服务）
Claude Code	Claude Code
clock speed	运转节奏（clock speed）
Coda	Coda（Grammarly 收购的生产力工具）
contact sync	通讯录同步（contact sync）
core loop	核心循环（core loop）
current user retention rate	现有用户留存率
Cursor	Cursor（AI 代码编辑器）
D1 retention	D1 留存率
Danny	Danny（Chess.com 联合创始人）
DeepBlue	DeepBlue（IBM 的国际象棋程序）
demand gen	需求挖掘（demand gen）
dogfooding	内部试用（dogfooding）
dormant users	沉睡用户
Dylan	Dylan（chess.com 产品经理）
Elo rating	Elo 等级分
Erik Allebest	Erik Allebest（Chess.com 相关）
experiment explorer	实验浏览器（experiment explorer）
explore and exploit framework	探索与利用（explore and exploit）框架
Failed Corner	失败角落（播客固定环节）
Figma Make	Figma Make（Figma 的 AI 功能）
freemium	免费增值（freemium）
game review	对局复盘（game review）
Garry Kasparov	Garry Kasparov（国际象棋世界冠军）
GitHub Copilot	GitHub Copilot
goal lock zone	黄金锁定区（goal lock zone）
Green Machine	Green Machine（Duolingo 内部产品开发手册）
Intercom	Intercom（客户支持平台）
Jackson	Jackson（Duolingo 相关人员）
JD	职位描述（JD）
Jorge Mazal	Jorge Mazal（Duolingo 增长领域同事）
Kayla	Kayla（Grammarly 订阅转化团队产品经理）
Lovable	Lovable（AI 原型工具）
Magnus Carlsen	Magnus Carlsen（国际象棋特级大师）
MCP	MCP（Model Context Protocol）
metagame	元游戏（metagame）
monetization	变现
Nermal	Nermal（Grammarly 的工程搭档）
Nick Turley	Nick Turley（ChatGPT 负责人）
Noam Levinsky	Noam Levinsky（Grammarly 相关）
perfect pitch	绝对音感（perfect pitch）
placement test	定级测试（placement test）
product-led sales	产品驱动型销售（product-led sales）
Queen’s Gambit	《后翼弃兵》（Netflix 剧集）
resurrected users	回归用户
retention	留存
reverse trials	反向试用（reverse trials）
self-serve	自助服务（self-serve）
Shishir	Shishir（Grammarly 新任 CEO）
Stockfish	Stockfish（国际象棋引擎）
Superhuman	Superhuman（Grammarly 收购的邮件客户端）
text to SQL	text to SQL（自然语言转 SQL 查询）
upsell	追加销售（upsell）
V0	V0（AI 原型工具）
vibes	直觉感（vibes）
virality	病毒式传播（virality）

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Reformatted by reformat_english.py